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Related Experiment Video

Updated: Apr 4, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

ST-Align: A time series and text alignment framework for cross-subject multivariate time series classification.

Zhenghuang Wu¹, Tao Zhang¹, Ke Li¹

¹School of Aeronautic Science and Engineering, Beihang University, Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society

|April 3, 2026

Summary

We introduce ST-Align, a framework aligning time series and text labels for better multivariate time series classification. This approach enhances cross-subject performance by leveraging large language models and achieving state-of-the-art results.

Keywords:

Cross-modal alignment Large language model (LLM)Multivariate time series classification

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Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

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Published on: July 22, 2025

Area of Science:

Machine Learning
Data Science
Artificial Intelligence

Background:

Multivariate Time Series Classification (MTSC) methods often overlook semantic information in text labels, limiting performance.
Existing unimodal approaches struggle with complex patterns and generalization in cross-subject scenarios.

Purpose of the Study:

To develop an ST-Align framework for cross-subject MTSC by aligning time series data with text labels.
To improve MTSC accuracy by integrating semantic information and leveraging large language model (LLM) knowledge.

Main Methods:

A two-stage alignment approach: fine-grained token alignment for local information and contrastive learning for Token-Prototype alignment.
Utilizing LLM prior knowledge to enhance semantic understanding and classification accuracy.

Main Results:

ST-Align achieved state-of-the-art (SOTA) performance on unseen cross-subject datasets.
Models incorporating LLM prior knowledge showed improved effectiveness, with average accuracies of 86.3% and 88.4%.

Conclusions:

The proposed ST-Align framework effectively integrates semantic information from text labels into MTSC.
Leveraging LLMs and alignment strategies significantly enhances performance in cross-subject multivariate time series classification.